In order to explain the prior art of object recognition in this section, face personal recognition by recognizing the acquired face image will be taken as an example. In general, there are two approaches to face recognition using a face image.
The first approach is a pattern matching method which captures a face as an image pattern expressed by two-dimensional arrays of density values of respective pixels, and performs recognition by matching image patterns. As a typical example of the pattern matching method, an eigenface method using PCA (Principal Component Analysis) (e.g., see U.S. Pat. No. 5,164,992) is taken, and the basic framework of the eigenface method will be described below.
The eigenface method applies PCA to the intensity value patterns of a large number of face images to obtain an orthonormal basis called an eigenface. Using the orthonormal basis, KL (Karhunen Loeve) expansion is applied to the intensity pattern of an arbitrary face image to obtain dimensionally compressed vectors of the pattern Finally, using the vectors as feature vectors for recognition, recognition is made by a statistical process between the feature vectors of an input pattern and registered patterns, which are registered in advance. The basic framework of the eigenface method has been described. This PCA based scheme must obtain an eigenface (average face) from a large number of face images in advance, and illumination variations and spatial layout variations of face images used to generate an eigenface influence the precision.
As the second approach, a feature-based method that performs recognition by matching feature vectors which numerically express the shapes of features and their spatial layout relationship by extracting feature points indicating features such as eyes, mouth, and nose of a face. As a typical example of the feature-based method, a scheme based on the Dynamic link architecture (e.g., see U.S. Pat. No. 6,356,659) is taken, and the basic framework of the scheme will be explained below.
In this scheme, a Gabor filter which extracts the periodicity and directionality of texture from a large number of sampling points (e.g., the eyes, mouth, nose, and outline of the face) set on a face pattern is applied to obtain local texture information as feature vectors. A graph which associates sampling points with nodes is calculated, and is built by applying feature vectors as the spatial layout information of the sampling points and attribute values of the nodes corresponding to the sampling points. The recognition process is implemented by elastically deforming the spatial layout information among nodes between an input pattern and the graphs of registered patterns which are registered in advance, and selecting a registered pattern with highest similarity (Dynamic graph matching). The basic framework of the scheme based on the Dynamic link architecture has been described.
However, since the scheme based on the Dynamic link architecture requires complicated numerical calculations in calculation of the attribute values at the sampling points and the processing of Dynamic graph matching, the operation cost increases depending on the courses of these processes.